{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:42:45Z","timestamp":1760060565314,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National major special project of China","award":["2024ZD0714401","2022YFB3303303","P2024-001"],"award-info":[{"award-number":["2024ZD0714401","2022YFB3303303","P2024-001"]}]},{"name":"National Key Research and Development Program of the Ministry of Science and Technology of China","award":["2024ZD0714401","2022YFB3303303","P2024-001"],"award-info":[{"award-number":["2024ZD0714401","2022YFB3303303","P2024-001"]}]},{"name":"Key Open Fund of China State Key Laboratory of Materials Processing and Die and Mould Technology","award":["2024ZD0714401","2022YFB3303303","P2024-001"],"award-info":[{"award-number":["2024ZD0714401","2022YFB3303303","P2024-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Accurate fault diagnosis of rotating machinery in complex environments and under changing operating conditions remains a key challenge in industrial systems. In this paper, we propose a novel fault diagnosis algorithm named dual-task cognitive cost sensitivity (DCCS), designed for high-accuracy diagnosis of rotary bearing faults and small-sample scenarios under variable working conditions. The method integrates four modules: CNN for local feature extraction, LSTM for temporal features, Softmax for classification, and a DCCS-based hyperparameter optimization module. A dual-task learning objective is formulated by combining losses from both full-condition and few-shot variable-condition datasets, with adaptive cost-sensitive weighting to balance learning focus. The integration of cognitive cost sensitivity with transfer learning enhances the model\u2019s adaptability, allowing it to flexibly generalize across different operating conditions. Experiments on the CWRU dataset demonstrate that the method achieves 99.33% accuracy within fewer training epochs and shows strong robustness to noise. Compared with mainstream optimization methods, DCCS offers higher efficiency with reduced computation time. In cross-condition diagnosis, it improves accuracy by up to 10.94 percentage points over the original Alpha Evolution algorithm, effectively addressing the challenge of limited samples in varying environments.<\/jats:p>","DOI":"10.3390\/bdcc9090232","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T08:40:15Z","timestamp":1757407215000},"page":"232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Variable Working Condition Fault Diagnosis Method for Rotating Machinery Based on Dual-Task Cognitive Cost Sensitivity"],"prefix":"10.3390","volume":"9","author":[{"given":"Qianwen","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1393-5388","authenticated-orcid":false,"given":"Jinghua","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China"},{"name":"Zhejiang Key Laboratory of Advanced Equipment Manufacturing and Measurement Technology, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Shuyou","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China"},{"name":"Zhejiang Key Laboratory of Advanced Equipment Manufacturing and Measurement Technology, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8147-9954","authenticated-orcid":false,"given":"Xiaojian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China"},{"name":"Zhejiang Key Laboratory of Advanced Equipment Manufacturing and Measurement Technology, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5745-5530","authenticated-orcid":false,"given":"Kang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China"},{"name":"Zhejiang Key Laboratory of Advanced Equipment Manufacturing and Measurement Technology, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1016\/j.eswa.2007.08.072","article-title":"A new approach to intelligent fault diagnosis of rotating machinery","volume":"35","author":"Lei","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s10845-013-0829-8","article-title":"Combined Intelligent Methods Based on Wireless Sensor Networks for Condition Monitoring and Fault Diagnosis","volume":"26","year":"2015","journal-title":"J. Intell. Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3083190","DOI":"10.1155\/2021\/3083190","article-title":"Application of Rotating Machinery Fault Diagnosis Based on Deep Learning","volume":"2021","author":"Cui","year":"2021","journal-title":"Shock Vib."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3510710","DOI":"10.1109\/TIM.2021.3055802","article-title":"Novel Convolutional Neural Network (NCNN) for the Diagnosis of Bearing Defects in Rotary Machinery","volume":"70","author":"Kumar","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Amaya-Sanchez, Q., Argumedo, M.J.d.M., Aguilar-Lasserre, A.A., Martinez, O.A.R., and Arroyo-Figueroa, G. (2024). Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8110145"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108119","DOI":"10.1016\/j.ress.2021.108119","article-title":"Prognostics and Health Management (PHM): Where Are We and Where Do We (Need to) Go in Theory and Practice","volume":"218","author":"Zio","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29969","DOI":"10.1109\/JSEN.2023.3326112","article-title":"Rotating Machinery Fault Diagnosis under Time-Varying Speeds: A Review","volume":"23","author":"Liu","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_8","unstructured":"Goodman, M.A., Bishop, W., and Mohr, G. (2015). System for Bearing Fault Detection. (US9200979B2), U.S. Patent."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112346","DOI":"10.1016\/j.measurement.2022.112346","article-title":"A review of the application of deep learning in intelligent fault diagnosis of rotating machinery","volume":"206","author":"Zhu","year":"2023","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.renene.2022.12.049","article-title":"Fault Detection of Offshore Wind Turbine Drivetrains in Different Environmental Conditions through Optimal Selection of Vibration Measurements","volume":"203","author":"Dibaj","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6263","DOI":"10.1109\/TII.2020.2967822","article-title":"Gearbox Fault Diagnosis Using a Deep Learning Model with Limited Data Sample","volume":"16","author":"Saufi","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jalayer, M., Kaboli, A., Orsenigo, C., and Vercellis, C. (2022). Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery. Machines, 10.","DOI":"10.3390\/machines10040237"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2010.07.017","article-title":"Rolling Element Bearing Diagnostics\u2014A Tutorial","volume":"25","author":"Randall","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5618","DOI":"10.1109\/JSEN.2017.2727638","article-title":"Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults from Vibration Signal","volume":"17","author":"Nayana","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1049\/iet-epa.2018.5274","article-title":"Rolling Bearing Fault Detection of Electric Motor Using Time Domain and Frequency Domain Features Extraction and ANFIS","volume":"13","author":"Helmi","year":"2019","journal-title":"IET Electr. Power Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1007\/s10489-025-06363-y","article-title":"Metallic Surface Defect Detection via NWD-WIoU Based on Grayscale Co-Generation Entropy Gain","volume":"55","author":"Xu","year":"2025","journal-title":"Appl. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"150248","DOI":"10.1109\/ACCESS.2020.3016888","article-title":"Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.1109\/JSEN.2020.3030910","article-title":"Fault diagnosis of rolling bearings based on an improved stack autoencoder and support vector machine","volume":"21","author":"Cui","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, H., Sun, W., He, L., and Zhou, J. (2022). Rolling Bearing Fault Diagnosis Using Multi-Sensor Data Fusion Based on 1D-CNN Model. Entropy, 24.","DOI":"10.3390\/e24050573"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ponomareva, V., Druzhina, O., Logunov, O., Rudnitskaya, A., Bobrova, Y., Andreev, V., and Karimov, T. (2024). Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8080082"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Khan, W.A., Chung, S.-H., Liu, S.Q., Masoud, M., and Wen, X. (2025). Smoothing and Matrix Decomposition-Based Stacked Bidirectional GRU Model for Machine Downtime Forecasting. IEEE Trans. Syst. Man Cybern. Syst., early access.","DOI":"10.1109\/TSMC.2025.3582768"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1177\/1475921718788299","article-title":"Deep Variational Auto-Encoders: A Promising Tool for Dimensionality Reduction and Ball Bearing Elements Fault Diagnosis","volume":"18","author":"Meruane","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sun, L., Xin, Y., Chen, T., and Feng, B. (2023). Rolling Bearing Fault Feature Selection Method Based on a Clustering Hybrid Binary Cuckoo Search. Electronics, 12.","DOI":"10.3390\/electronics12020459"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e17584","DOI":"10.1016\/j.heliyon.2023.e17584","article-title":"Machine Learning for Fault Analysis in Rotating Machinery: A Comprehensive Review","volume":"9","author":"Das","year":"2023","journal-title":"Heliyon"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1108\/IMDS-07-2019-0361","article-title":"Machine Learning Facilitated Business Intelligence (Part I): Neural Networks Learning Algorithms and Applications","volume":"120","author":"Khan","year":"2019","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Molaei, S., Cirillo, S., and Solimando, G. (2024). Cancer detection using a new hybrid method based on pattern recognition in MicroRNAs combining particle swarm optimization algorithm and artificial neural network. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8030033"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Malekzadeh, A., Zare, A., Yaghoobi, M., and Alizadehsani, R. (2021). Automatic diagnosis of epileptic seizures in EEG signals using fractal dimension features and convolutional autoencoder method. Big Data Cogn. Comput., 5.","DOI":"10.3390\/bdcc5040078"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"25132","DOI":"10.1109\/ACCESS.2023.3255417","article-title":"Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning","volume":"11","author":"Maior","year":"2023","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TIM.2018.2836058","article-title":"RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits","volume":"68","author":"Binu","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1007\/s00500-021-06307-x","article-title":"Intelligent Bearing Fault Diagnosis Using Swarm Decomposition Method and New Hybrid Particle Swarm Optimization Algorithm","volume":"26","author":"Amini","year":"2022","journal-title":"Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127474","DOI":"10.1016\/j.eswa.2025.127474","article-title":"Super-Resolution 3D Reconstruction from Low-Dose Biomedical Images Based on Expertized Multi-Layer Refining","volume":"281","author":"Xu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning Spatiotemporal Features with 3D Convolutional Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_34","unstructured":"Grave, E., Joulin, A., Ciss\u00e9, M., Grangier, D., and J\u00e9gou, H. (2017). Efficient Softmax Approximation for GPUs. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109202","DOI":"10.1016\/j.engappai.2024.109202","article-title":"Alpha Evolution: An Efficient Evolutionary Algorithm with Evolution Path Adaptation and Matrix Generation","volume":"137","author":"Gao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"93155","DOI":"10.1109\/ACCESS.2020.2990528","article-title":"Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset with Deep Learning Approaches: A Review","volume":"8","author":"Neupane","year":"2020","journal-title":"IEEE Access"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/9\/232\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:42:09Z","timestamp":1760035329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/9\/232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":36,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["bdcc9090232"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9090232","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,9,8]]}}}